Table of Contents
Safety, Volume 4, Issue 2 (June 2018)
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Cover Story (view full-size image) We developed a binary logit regression model to predict accident likelihood at highway rail grade [...] Read more. We developed a binary logit regression model to predict accident likelihood at highway rail grade crossings in North Dakota by incorporating various contributing factors. One important factor, population within five miles of crossings, was found to have a positive relationship with crash likelihood. To check the sensitivity of buffer size, i.e., population within the buffer, we repeated the same process for population within a one-mile radius, but the results did not show a significant relationship. View this paper